Yannick Pencolé

Incremental decentralized diagnosis approach for the supervision of a telecommunication network

This paper extends the decentralized approach proposed by Pencole (2000) for diagnosing discrete-event systems. The incrementality issue, which is a crucial point in the context of online diagnosis, is discussed and two solutions are proposed.

A Model-based Approach for Diagnosing Fault in Web Service Processes

Web service orchestration languages are de?ned to describe business processes composed of Web services. A business process can fail for many reasons, such as faulty Web services or mismatching messages. It is important to ?nd out which Web services are responsible for a failed business process because we could penalize these Web services and exclude them from the business process in the future. In this paper, we propose a model-based approach to diagnose the faults in a Web service-composed business process. We convert a Web service orchestration language, BPEL4WS, into synchronized automata, so that we have a formal description of the topology and variable dependency of the business process. After an exception is thrown, the diagnoser can calculate the business process execution trajectory based on the formal model and the observed evolution of the business process. The faulty Web services are deduced from the variable dependency on the execution trajectory.

Monitoring Web service networks in a model-based approach

The goal of Web service effort is to achieve universal interoperability between applications by using Web standards: this emergent technology is a promising way to integrate business applications. A business process can then be seen as a set of Web services that could belong to different companies and interact with each other by sending messages. In that context, neither a global model nor a global mechanism is available to monitor and trace faults when the business process fails. In this paper, we address this issue and propose to use model-based reasoning approaches on discrete-event systems (DES). This paper presents an automatic method to model Web service behaviors and their interactions as a set of synchronized discrete-event systems. This modeling is the first step before tracing the evolution of the business process and diagnosing business process faults.

Monitoring and Active Diagnosis for Discrete-Event Systems

Abstract This article presents an original way to enrich the monitoring of discrete-event systems named active diagnosis. The objective of on-line active diagnosis is to find an admissible sequence of actions (or plan) that refines the diagnosis without radically changing the mission plan. This paper has two major contributions. First, active diagnosis is formally defined in the finite-state automata theory framework. This leads to the definition of a complete active diagnoser which on line monitors the system behavior. Secondly, from the complete active diagnoser is defined a planning problem. The goal is to find a conditional plan that defines an admissible sequence of actions. These actions are applied on the physical system and may conduct the active diagnoser into a diagnosable region.

A decentralized model-based diagnostic tool for complex systems

We address the problem of diagnosing complex discrete-event systems such as telecommunication networks. Given a flow of observations from the system, the goal is to explain those observations by identifying and localizing possible faults. Several model-based diagnosis approaches deal with this problem but they need the computation of a global model which is not feasible for complex systems like telecommunication networks. Our contribution is the proposal of a decentralized approach which permits to carry out an on-line diagnosis without computing the global model. This paper describes the implementation of a tool based on this approach. Given a decentralized model of the system and a flow of observations, the program analyzes the flow and computes the diagnosis in a decentralized way. We also present experimental results based on a real system.

Diagnosis and prognosis for the maintenance of complex systems

This paper adresses the problem of maintenance of a complex and heterogeneous system like an aircraft. To optimise maintenance, it is required to embed in the system a health monitoring system that implements diagnostic and prognostic capabilities. This paper thus presents a formal characterisation of the diagnostic and prognostic problems in order to support the maintenance of a complex system.

Assistance for the design of a diagnosable component-based system

Diagnosability of component-based systems is a property that characterises the ability to diagnose fault events given a flow of observations. In this paper, we use model-based reasoning techniques and we propose a theoretical framework to analyse diagnosability in a decentralised way. We then introduce an algorithm that performs diagnosability analyses and provides useful information for the design of a diagnosable component-based system

A theory of meta-diagnosis: reasoning about diagnostic systems

In Model-Based Diagnosis, a diagnostic algorithm is typically used to compute diagnoses using a model of a real-world system and some observations. Contrary to classical hypothesis, in real-world applications it is sometimes the case that either the model, the observations or the diagnostic algorithm are abnormal with respect to some required properties; with possibly huge economical consequences. Determining which abnormalities exist constitutes a meta-diagnostic problem. We contribute, first, with a general theory of meta-diagnosis with clear semantics to handle this problem. Second, we propose a series of typically required properties and relate them between themselves. Finally, using our meta-diagnostic framework and the studied properties and relations, we model and solve some common meta-diagnostic problems.

MEDITO: A Logic-Based Meta-diagnosis Tool

In every Model-Based Diagnosis (MBD) approach, a model of a real-world system and some observations of such a system are used by a diagnostic algorithm to compute diagnoses. Contrary to MBD classical hypotheses, real-world applications provide us with empirical data suggesting that diagnostic systems, i.e. a model, observations and a diagnostic algorithm, are sometimes abnormal with respect to some required properties. This is where Meta-Diagnosis comes into play with a theory to determine abnormalities in diagnostic systems. Unfortunately, Artificial Intelligence lacks of a tool putting meta-diagnosis theory to practice. Our first contribution in this paper is such a tool, called MEDITO. Moreover, we provide a real-world example of MEDITOs application at meta-diagnosing an Airbus landing gear extraction and retraction system with successful results.

TOWARDS LOW-COST FAULT DIAGNOSIS IN LARGE COMPONENT-BASED SYSTEMS 1

Abstract We address the problem of fault diagnosis in discrete-event systems. Our contribution is the development of a set of specialised diagnosers whose computation is much more realistic than that of the classical diagnoser. A specialised diagnoser is devoted to the diagnosis of one particular type of fault and is based on the observation of only a subpart of the system.